






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 		SCREAMING_SNAKE_CASE__	(  __lowerCAmelCase					,      __lowerCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  1


  @register_to_config
  def __init__(      self		:			Dict       ,				lowerCamelCase		:			str=2000       ,				lowerCamelCase		:			List[Any]=0.1       ,				lowerCamelCase		:			Any=20       ,				lowerCamelCase		:			List[str]=1E-3							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, torch.device] = None							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    torch.linspace(1       ,				self.config.sampling_eps       ,				lowerCamelCase_       ,				device=lowerCamelCase_							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[int]=None							)						->	List[str]:


         """simple docstring"""


         if self.timesteps is None:
                raise ValueError(
                    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

         # TODO(Patrick) better comments + non-PyTorch
         # postprocess model score
         _UpperCAmelCase							=    (
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         _UpperCAmelCase							=    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
         _UpperCAmelCase							=    std.flatten()
         while len(std.shape							) < len(score.shape							):
                _UpperCAmelCase							=    std.unsqueeze(-1							)
         _UpperCAmelCase							=    -score / std

         # compute
         _UpperCAmelCase							=    -1.0 / len(self.timesteps							)

         _UpperCAmelCase							=    self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
         _UpperCAmelCase							=    beta_t.flatten()
         while len(beta_t.shape							) < len(x.shape							):
                _UpperCAmelCase							=    beta_t.unsqueeze(-1							)
         _UpperCAmelCase							=    -0.5 * beta_t * x

         _UpperCAmelCase							=    torch.sqrt(lowerCamelCase_							)
         _UpperCAmelCase							=    drift - diffusion**2 * score
         _UpperCAmelCase							=    x + drift * dt

         # add noise
         _UpperCAmelCase							=    randn_tensor(x.shape       ,				layout=x.layout       ,				generator=lowerCamelCase_       ,				device=x.device       ,				dtype=x.dtype							)
         _UpperCAmelCase							=    x_mean + diffusion * math.sqrt(-dt							) * noise

         return x, x_mean





  def __len__(      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         return self.config.num_train_timesteps
import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE 		=      logging.getLogger()

_SCREAMING_SNAKE_CASE 		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    os.makedirs(lowerCamelCase_   ,   exist_ok=lowerCamelCase_				)
    UpperCamelCase			  =      {"""source""": """What is love ?""", """target""": """life"""}
    UpperCamelCase			  =      {"""train""": 12, """val""": 2, """test""": 2}
    for split in ["train", "test", "val"]:
     for field in ["source", "target"]:
      UpperCamelCase			  =      """\n""".join([contents[field]] * n_lines[split]				)
      with open(os.path.join(lowerCamelCase_   ,   f"""{split}.{field}"""				)   ,   """w"""				) as f:
       f.write(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str = "pytorch"				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_auto_remove_tmp_dir()
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """output"""				)
    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """data"""				)
    self._create_dummy_data(data_dir=lowerCamelCase_				)

    UpperCamelCase			  =      f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

    if gpus > 0:
     testargs.append(f"""--gpus={gpus}"""				)
     if is_apex_available():
      testargs.append("""--fp16"""				)
    else:
     testargs.append("""--gpus=0"""				)
     testargs.append("""--distributed_backend=ddp_cpu"""				)
     testargs.append("""--num_processes=2"""				)

    UpperCamelCase			  =      [sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
    execute_subprocess_async(lowerCamelCase_   ,   env=self.get_env()				)

    UpperCamelCase			  =      os.path.join(lowerCamelCase_   ,   """metrics.json"""				)
    with open(lowerCamelCase_				) as f:
     UpperCamelCase			  =      json.load(lowerCamelCase_				)
    return result







   @require_torch_gpu
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=2				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)







   @require_torch_multi_gpu
   @require_ray
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self._run_finetune(gpus=1   ,   distributed_retriever="""ray"""				)
    self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]   ,   0.2				)
